We explain diffusion models and flow-matching models side by side to highlight the key differences between them. Flow-Matching Models are the new generation of AI image generators that are quickly replacing diffusion models — they take everything diffusion did well, but make it faster, smoother, and deterministic. AI Coffee Break Merch! 🛍️ Text to image diffusion models: Useful deeper reading: • 🌊 Lipman et al., “Flow Matching for Generative Modeling” (2023) — • 🧮 Kingma and Gao, "Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation" (2022) — • ⚡ Esser et al, "Scaling Rectified Flow Transformers for High-Resolution Image Synthesis" (2024) — Thanks to our Patrons who support us in Tier 2, 3, 4: 🙏 Vignesh Valliappan, Ivan Janov, Sunny Dhiana, Andy Ma Outline: 00:00 Difference between Flow-matching and Diffusion 01:07 Training Diffusion Models 05:45 Inference for Diffusion Models 09:03 Training Flow-Matching 11:55 Inference with Flow-Matching 14:02 Side-by-Side Comparison ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🔥 Optionally, pay us a coffee to help with our Coffee Bean production! ☕ Patreon: Ko-fi: Join this channel as a Bean Member to get access to perks: ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🔗 Links: AICoffeeBreakQuiz: Twitter / X: LinkedIn: Threads: @ Bluesky: Reddit: YouTube: Substack: Web: #AICoffeeBreak #MsCoffeeBean #MachineLearning #AI #research​ Video editing: Nils Trost